Removal of hazardous textile dye from simulated wastewater by municipal organic solid waste charcoal using machine learning approaches: Kinetics, isotherm, and thermodynamics.

This study focuses on the probable use of municipal organic solid waste charcoal (MOSWC) as an adsorbent for Methyl orange (MO) adsorption. The prepared MOSWC is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), adsorbate concentration (60-140 mg/L), adsorbent dose (1-5 g/L), pH (3-11), and temperature (25-60 °C). The high coefficient value (R2 = 0.96) of the process optimization model suggests that this model was significant, where pH and adsorbent dose expressively stimulus adsorption efficiency including 40.11 mg/g at pH (3), MO concentration (100 mg/L), and MOSWC dose (1 g/L). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected values. The highest monolayer adsorption capacity of MO was 90.909 mg/g. Pseudo-second-order was the well-suited kinetics, where Langmuir isotherm could explain better for equilibrium adsorption data. Thermodynamic study shows MO adsorption is favourable, exothermic, and spontaneous. Finally, this study indicates that MOSWC could be a potential candidate for the adsorption of MO from wastewater.

Black carbon derived PET plastic bottle waste and rice straw for sorption of Acid Red 27 dye: Machine learning approaches, kinetics, isotherm and thermodynamic studies.

This study focuses on the probable use of PET waste black carbon (PETWBC) and rice straw black carbon (RSBC) as an adsorbent for Acid Red 27 (AR 27) adsorption. The prepared adsorbent is characterized by FE-SEM and FT-IR. Batch adsorption experiments were conducted with the influencing of different operational conditions namely time of contact (1-180 min), AR 27 concentration (5-70 mg/L), adsorbent dose (0.5-20 g/L), pH (2-10), and temperature (25-60°C). High coefficient value [PETWBC (R2 = 0.94), and RSBC (R2 = 0.97)] of process optimization model suggesting that this model was significant, where pH and adsorbent dose expressively stimulus removal efficiency including 99.88, and 99.89% for PETWBC, and RSBC at pH (2). Furthermore, the machine learning approaches (ANN and BB-RSM) revealed a good association between the tested and projected value. Pseudo-second-order was the well-suited kinetics, where Freundlich isotherm could explain better equilibrium adsorption data. Thermodynamic study shows AR 27 adsorption is favourable, endothermic, and spontaneous. Environmental friendliness properties are confirmed by desorption studies and satisfactory results also attain from real wastewater experiments. Finally, this study indicates that PETWBC and RSBC could be potential candidates for the adsorption of AR 27 from wastewater.

Human health risk ​and receptor model-oriented sources of heavy metal pollution in commonly consume vegetable and fish species of high Ganges river floodplain agro-ecological area, Bangladesh.

This study was intended to assess heavy metal contents and sources in commonly consumed vegetables and fish collected from the Jashore district of Bangladesh and to evaluate the probable human health risks via the ingesting of those vegetables and fish species. A total of 130 vegetable and fish samples were analyzed for As, Mn, Cu, Cr, Ni, and Pb concentration by an atomic absorption spectrophotometer. Metals and metalloids like As, Pb, and Cr in vegetable species were greater than the maximum allowable concentration (MAC), while Pb and cu in fish species exceeded the MAC. Pollution evaluation index values were ranges from 0.40-10.35 and 1.53-2.78 for vegetable and fish species, respectively, indicating light to serious pollution. Lactuca sativa followed by Cucurbita moschata, Amaranthus gangeticus for vegetables and Channa punctate, Oreochromis mossambicus, followed by Dendrobranchiata for fish are the most contaminated food items. The positive matrix factorization model showed that As (81.9%), Ni (48%), Cr (49.6%), Mn (46%), Pb (44.3%), and Cu (44.4%) for vegetable species and As (86.9%), Ni (90.5%), Mn (67.6%), Pb (65.3%), Cr (57%) and Cu (46.2%) for fish species were resulting from agrochemical, atmospheric emission, irrigation, contaminated feed, and mixed sources. The self-organizing map and principle component analysis indicates three spatial patterns e.g., As-Mn-Cu, Pb-Cr, and Ni in vegetables and As-Mn-Cr, Cu-Ni, and Pb in fish samples. The THQ values for single elements were less than 1 (except As for vegetables and Pb for fish species) for all food items but the HI values for all of the vegetables (2.18E+00 to 2.04E+01) and fish (1.07E+00 to 9.39E+00) samples were exceeded the USEPA acceptable risk level (HI > 1E+00). While the cancer risks only induced by As for all vegetables and fish species, which exceeded the USEPA safe level (TCR>1E-04). Sensitivity analysis indicates that metal concentration was the most responsible factor for carcinogenic risk.